A non-targeted metabolomic approach based on reversed-phase liquid chromatography-mass spectrometry to evaluate coffee roasting process
Autores
Pérez Miguez, Raquel; Sánchez López, Elena; Plaza del Moral, Merichel; Castro Puyana, María; Marina Alegre, María LuisaIdentificadores
Enlace permanente (URI): http://hdl.handle.net/10017/48127DOI: 10.1007/s00216-018-1405-z
ISSN: 1618-2642
Fecha de publicación
2018-10Filiación
Universidad de Alcalá. Departamento de Química Analítica, Química Física e Ingeniería QuímicaCita bibliográfica
Analytical and Bioanalytical Chemistry, 2018, v. 410, n. 30, p. 7859-7870
Palabras clave
Non-targeted metabolomics
Liquid chromatography
High-resolution mass spectrometry
Coffee beans
Roasting process
Proyectos
info:eu-repo/grantAgreement/CAM//S2018%2FBAA-4393/ES/ESTRATEGIAS INTEGRADAS PARA LA MEJORA DE LA CALIDAD, LA SEGURIDAD Y LA FUNCIONALIDAD DE LOS ALIMENTOS: HACIA UNA ALIMENTACIÓN SALUDABLE/AVANSECAL-II
info:eu-repo/grantAgreement/UAH//CCG2015%2FEXP-032/ES/DESARROLLO DE UNA ESTRATEGIA METABOLÓMICA NO DIRIGIDA POR ELECTROFORESIS CAPILAR ACOPLADA A ESPECTROMETRÍA DE MASAS PARA LA CARACTERIZACIÓN DE ALIMENTOS DE ALTO CONSUMO
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Derechos
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
© Springer
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
In this work, a non-targeted metabolomics approach based on the use of reversed-phase liquid chromatography coupled to a high-resolution mass spectrometer has been developed to provide the characterization of coffee beans roasted at three different levels (light, medium, and dark). In this way, it was possible to investigate how metabolites change during the roasting process in order to identify those than can be considered as relevant markers. Twenty-five percent methanol was selected as extracting solvent since it provided the highest number of molecular features. In addition, the effect of chromatographic and MS parameters was evaluated in order to obtain the most adequate separation and detection conditions. Data were analyzed using both non-supervised and supervised multivariate statistical methods to point out the most significant markers that allow group discrimination. A total of 24 and 33 compounds in positive and negative ionization modes, respectively, demonstrated to be relevant markers; most of them were from the hydroxycinnamic acids family.
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